Neurology

Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia.

Dementia poses a growing challenge for health services but remains stigmatized and under-recognized. Digital technologies to aid the earlier detection of dementia are approaching market. These include traditional cognitive screening tools presented on mobile devices, smartphone native applications, passive data collection from wearable, in-home and in-car sensors, as well as machine learning techn...

Contribution of Retzius-sparing robot-assisted radical prostatectomy to the mechanism of urinary continence as demonstrated by dynamic MRI.

Retzius-sparing robot-assisted radical prostatectomy (RARP) has been reported to exhibit better postoperative urinary continence, but the reasons behind this are unknown. This study included 254 cases who underwent RARP and underwent postoperative dynamic MRI. We measured the urine loss ratio (ULR) immediately after postoperative urethral catheter removal and investigated its affecting factors and...

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson's Disease Off and On Treatment: Machine vs. Deep-Learning Comparison.

Parkinson's Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different ...

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG.

. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) ...

A low-power and flexible bioinspired artificial sensory neuron capable of tactile perceptual and associative learning.

Biomimetic haptic neuron systems have received a lot of attention from the booming artificial intelligence industry for their wide applications in per...

Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results.

Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same...

CNTSeg: A multimodal deep-learning-based network for cranial nerves tract segmentation.

The segmentation of cranial nerves (CNs) tracts based on diffusion magnetic resonance imaging (dMRI) provides a valuable quantitative tool for the ana...

Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.

In brain-computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many ...

Nurses' perception towards care robots and their work experience with socially assistive technology during COVID-19: A qualitative study.

This study aimed to explore nurses' perceptions towards care robots and their work experiences in caring for older adults who use socially assistive t...

Interpretable machine learning for dementia: A systematic review.

INTRODUCTION: Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact....

Real-Time Automated Segmentation of Median Nerve in Dynamic Ultrasonography Using Deep Learning.

OBJECTIVE: The morphological dynamics of the median nerve across the level extracted from dynamic ultrasonography are valuable for the diagnosis and e...

The role of Artificial intelligence in the assessment of the spine and spinal cord.

Artificial intelligence (AI) application development is underway in all areas of radiology where many promising tools are focused on the spine and spi...

Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI.

Alzheimer's disease (AD) is the most common form of dementia. Computer-aided diagnosis (CAD) can help in the early detection of associated cognitive i...

Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI.

In recent years, deep learning-based feature representation methods have shown a promising impact on electroencephalography (EEG)-based brain-computer...

A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction.

Accurate early prediction of epileptic seizures can provide timely treatment for patients. Previous studies have mainly focused on a single temporal o...

Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities.

BACKGROUND: The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, y...

EEG emotion recognition using improved graph neural network with channel selection.

BACKGROUND AND OBJECTIVE: Emotion classification tasks based on electroencephalography (EEG) are an essential part of artificial intelligence, with pr...

SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry.

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutio...

Restoring arm function with a soft robotic wearable for individuals with amyotrophic lateral sclerosis.

Despite promising results in the rehabilitation field, it remains unclear whether upper limb robotic wearables, e.g., for people with physical impairm...

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State.

Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for d...